This study aimed to establish a risk model of hub genes to evaluate the prognosis of patients with cervical cancer. Based on TCGA and GTEx databases, the differentially expressed genes (DEGs) were screened and then analyzed using GO and KEGG analyses. The weighted gene co-expression network (WGCNA) was then used to perform modular analysis of DEGs. Univariate Cox regression analysis combined with LASSO and Cox-pH was used to select the prognostic genes. Then, multivariate Cox regression analysis was used to screen the hub genes. The risk model was established based on hub genes and evaluated by risk curve, survival state, Kaplan-Meier curve, and receiver operating characteristic (ROC) curve. We screened 1265 DEGs between cervical cancer and normal samples, of which 620 were downregulated and 645 were upregulated. GO and KEGG analyses revealed that most of the upregulated genes were related to the metastasis of cancer cells, while the downregulated genes mostly acted on the cell cycle. Then, WGCNA mined six modules (red, blue, green, brown, yellow, and gray), and the brown module with the most DEGs and related to multiple cancers was selected for the follow-up study. Eight genes were identified by univariate Cox regression analysis combined with the LASSO Cox-pH model. Then, six hub genes (SLC25A5, ENO1, ANLN, RIBC2, PTTG1, and MCM5) were screened by multivariate Cox regression analysis, and SLC25A5, ANLN, RIBC2, and PTTG1 could be used as independent prognostic factors. Finally, we determined that the risk model established by the six hub genes was effective and stable. This study supplies the prognostic value of the risk model and the new promising targets for the cervical cancer treatment, and their biological functions need to be further explored.

中文翻译：

---

基于WGCNA结合LASSO和Cox-PH分析的宫颈癌六基因预后模型的确定

本研究旨在建立hub基因的风险模型，以评估宫颈癌患者的预后。基于TCGA和GTEx数据库，筛选差异表达基因（DEG），然后使用GO和KEGG分析进行分析。然后使用加权基因共表达网络 (WGCNA) 对 DEG 进行模块化分析。结合LASSO和Cox-pH的单变量Cox回归分析用于选择预后基因。然后，多变量Cox回归分析用于筛选枢纽基因。基于hub基因建立风险模型，并通过风险曲线、生存状态、Kaplan-Meier曲线和受试者工作特征（ROC）曲线进行评估。我们在宫颈癌和正常样本之间筛选了 1265 个 DEG，其中 620 个下调，645 个上调。GO和KEGG分析显示，大部分上调基因与癌细胞的转移有关，而下调基因主要作用于细胞周期。然后，WGCNA 挖掘了六个模块（红色、蓝色、绿色、棕色、黄色和灰色），选择 DEG 最多且与多种癌症相关的棕色模块进行后续研究。通过单变量Cox回归分析结合LASSO Cox-pH模型鉴定了8个基因。然后，通过多元Cox回归分析筛选出6个枢纽基因（SLC25A5、ENO1、ANLN、RIBC2、PTTG1和MCM5），SLC25A5、ANLN、RIBC2和PTTG1可作为独立的预后因素。最后，我们确定由六个枢纽基因建立的风险模型是有效且稳定的。